A practical approach for updating an integrity-enforced operating system
Not on the map. Why?
Abstract (arXiv)
Trusted computing defines how to securely measure, store, and verify the integrity of software controlling a computer. One of the major challenges that make them hard to be applied in practice is the issue with software updates. Specifically, an operating system update causes the integrity violation because it changes the well-known initial state trusted by remote verifiers, such as integrity monitoring systems. Consequently, the integrity monitoring of remote computers becomes unreliable due to the high amount of false positives. We address this problem by adding an extra level of indirection between the operating system and software repositories. We propose a trusted software repository (TSR), a secure proxy that overcomes the shortcomings of previous approaches by sanitizing software packages. Sanitization consists of modifying unsafe installation scripts and adding digital signatures in a way software packages can be installed in the operating system without violating its integrity. TSR leverages shielded execution, i.e., Intel SGX, to achieve confidentiality and integrity guarantees of the sanitization process. TSR is transparent to package managers, and requires no changes in the software packages building and distributing processes. Our evaluation shows that running TSR inside SGX is practical; since it induces only ~1.18X performance overhead during package sanitization compared to the native execution without SGX. TSR supports 99.76% of packages available in the main and community repositories of Alpine Linux while increasing the total repository size by 3.6%.
Key facts. Cited by 2 papers in the map (data through 13 Oct 2026). First posted on arXiv 5 Jan 2021.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 2.
2 papers.
- 2021secureTF: A Secure TensorFlow Frameworkcited by 12
- 2021SecFL: Confidential Federated Learning using TEEscited by 3
- 2021SecFL: Confidential Federated Learning using TEEs3 Oct 2021
- 2021secureTF: A Secure TensorFlow Framework20 Jan 2021